Differential privacy [1]}, [2]}, [3]} has been increasingly accepted as the current standard for data privacy.
The central DP model assumes a trusted curator, which collects the clients' raw data and releases sanitized aggregated data.
The LDP model [4]}, [5]}, [6]} does not rely on collecting raw data anymore, which has a clear connection with the concept of randomized response [7]}.
In recent years, there have been several studies on the local DP setting, e.g., for frequency estimation of a single [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]} and multiple [15]}, [16]} attributes; mean estimation [17]}, [18]}, heavy hitter estimation [19]}, [20]}, and machine learning [21]}, [22]}.
